Active Learning Classification of Drifted Streaming Data

AbstractIn present times, data science become popular to support and improve decision-making process. Due to the accessibility of a wide application perspective of data streaming, class imbalance and concept drifting become crucial learning problems. The advent of deep learning (DL) models finds useful for the classification of concept drift in data streaming applications. This paper presents an effective class imbalance with concept drift detection (CIDD) using Adadelta optimizer-based deep neural networks (ADODNN), named CIDD-ADODNN model for the classification of highly imbalanced streaming data. The presented model involves four processes namely preprocessing, class imbalance handling, concept drift detection, and classification. The proposed model uses adaptive synthetic (ADASYN) technique for handling class imbalance data, which utilizes a weighted distribution for diverse minority class examples based on the level of difficulty in learning. Next, a drift detection technique called adaptive sliding window (ADWIN) is employed to detect the existence of the concept drift. Besides, ADODNN model is utilized for the classification processes. For increasing the classifier performance of the DNN model, ADO-based hyperparameter tuning process takes place to determine the optimal parameters of the DNN model. The performance of the presented model is evaluated using three streaming datasets namely intrusion detection (NSL KDDCup) dataset, Spam dataset, and Chess dataset. A detailed comparative results analysis takes place and the simulation results verified the superior performance of the presented model by obtaining a maximum accuracy of 0.9592, 0.9320, and 0.7646 on the applied KDDCup, Spam, and Chess dataset, respectively.

Download Full-text

Comparison of Active Learning Performance for Automatic Classification of Learning Contents

The Journal of Korean Institute of Information Technology ◽

10.14801/jkiit.2021.19.7.1 ◽

2021 ◽

Vol 19 (7) ◽

pp. 1-7

Author(s):

Eun-Sook Kang ◽

Dae-Sik Ko

Keyword(s):

Active Learning ◽

Automatic Classification ◽

Learning Performance

Download Full-text

Classification of Summer Crops Using Active Learning Techniques on Landsat Images in the Northwest of the Province of Buenos Aires

Communications in Computer and Information Science - Cloud Computing, Big Data & Emerging Topics ◽

10.1007/978-3-030-61218-4_10 ◽

2020 ◽

pp. 138-152

Author(s):

Lucas Benjamin Cicerchia ◽

María José Abasolo ◽

Claudia Cecilia Russo

Keyword(s):

Active Learning ◽

Buenos Aires ◽

Landsat Images ◽

Learning Techniques

Download Full-text

Classifying streaming data using grammar-based immune programming

10.32920/ryerson.14656533.v1 ◽

2021 ◽

Author(s):

Jaspreet Kaur Bassan

Keyword(s):

Active Learning ◽

Artificial Immune System ◽

Streaming Data ◽

Benchmark Problems ◽

Artificial Immune ◽

Training Process ◽

Real World Data ◽

Learning Technique ◽

Computationally Expensive ◽

Immune Programming

This work proposes a technique for classifying unlabelled streaming data using grammar-based immune programming, a hybrid meta-heuristic where the space of grammar generated solutions is searched by an artificial immune system inspired algorithm. Data is labelled using an active learning technique and is buffered until the system trains adequately on the labelled data. The system is employed in static and in streaming data environments, and is tested and evaluated using synthetic and real-world data. The performances of the system employed in different data settings are compared with each other and with two benchmark problems. The proposed classification system adapted well to the changing nature of streaming data and the active learning technique made the process less computationally expensive by retaining only those instances which favoured the training process.

Download Full-text